Study On Abnormal Situation Management System Based On SDG Integreted With Several Fault Diagnosis Methods

The petrochemical production process often emerge abnormal situation state due to sensor drift, equipment failure, operator error, process fluctuations or other reasons. It would cause product quality decline, production scheduling plan delayed, a serious accident occurs, even casualties and huge economic losses happens.How to timely identify the existence of abnormal situations, find the reasons leading to abnormal situations, predict the possible consequences, propose appropriate measures to assist operator to cope timely and correctly, avoiding serious effects appear, ensure that petrochemical production plant the long-term stable operation is the hot spots of current international study.Based on the results of predecessors research, supported by the national 863 project 2009AA04Z133 "Qualitative and Quantitative Study of Complex Fault Diagnosis", the research study mainly on signed directed graph (SDG, Signed Directed Graph) technology, combined with expert rules, fuzzy logic, principal component analysis etc. qualitative and quantitative fault diagnosis methods, explored the establishment of Abnormal Situation Management System implementation methods and evaluation tools.Abnormal Situation Management System is based on automatic monitoring, reasoning, forecasting and guidance as one integrated computer system, which has four levels of objectives:v keep production running normal;v when a volatility process occurs, can be resumed to normal state under the guidance of management system;v when a large deviation occurs, can enable process state in a safe state under the guidance of management system;v when an emergency situation or incident occurs, can minimize the seriousness of the impact the incident under the guidance of management systems.In order to achieve the above objectives, this paper had developed a state identification, the reasons for the diagnosis, possible consequences, and treatment measures etc. functional modules, combined with data acquisition module and expert knowledge management module, to:v when an abnormal situation occurs, in real time, quickly locate origin of the problem, assess the consequences, present the program and program argument;v having self-learning function, make the experience of how to cope with the abnormal situation to be inherited.v security information accumulated over the years, through data mining, can provide management with incident type, fault source distribution, incident probability, and so on the statistical information for security management to provide decision support.The core of Abnormal Situation Management System is the fast inference speed and accurate inference results of the reasoning inference engine. The fast inference speed depends on the rational algorithm of the inference engine, and the inference accuracy of the results depends on the accuracy of the model established. In order to verify the accuracy of the model, this paper maked a preliminary exploration of model validation and verification methods.The main work and achievements of this paper are as follows:1. Carried out a variety of SDG-based fault diagnosis method of fusion technology;2. Developed abnormal situation management system software platform which is based on Visual Studio;3. Established a qualitative and quantitative V&V (Verification & Validation) test platform;4. Established an Abnormal Situation Management System for CDU unit;5. Completed V&V of CDU Unit Abnormal Situation Management System in the qualitative and quantitative platform;6. Completed CDU Unit Abnormal Situation Management System application testing in the industrial field.